We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatically solve complex, dynamic problems. So, this work is concerned with the creation, investigation, and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. We then investigated its self-organising aspects, starting with an extension to the definition of Physical Complexity to include evolving agent populations. Next, we established stability of evolving agent populations over time, by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics. Further, we evaluated the diversity of the software agents within evolving agent populations. To conclude, we considered alternative augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating effect of a clustering catalyst on the evolutionary dynamics. We also studied the optimising effect of targeted migration on the ecological dynamics, through the indirect and emergent optimisation of the agent migration patterns. Overall, we have advanced the understanding of creating Digital Ecosystems, the self-organisation that occurs within them, and the optimisation of their Ecosystem-Oriented Architecture.

206 Pages, 73 figures, PhD